Data Lake: What Is it and How It Can Benefit Your Company


Marketers and tech gurus bombard us with new high-tech terms daily, telling us how we will fall behind if we don’t implement *the latest trendy topic* in our business. Blockchain, AR, VR, Metaverse, headless CMS, it really can be overwhelming to stay on top of all the changes.

While there are fads, some of the solutions you hear about are genuinely useful and can put your business to a new level if put into good use.

This article will tell you what is a data lake and how it can benefit your company. I will not only explain how you can benefit from this technology and how it differs from data warehousing, but also give you three data lake providers I found to be the best. Let’s roll!

What is a data lake?

data lake is a term that describes central storage, which contains a large amount of data kept in its native form. In data lakes, big data isn’t sorted but instead stored in a single location, hence the name “lake.”

At first glance, this sounds very impractical, as it may seem that a large pile of random unsorted data is pretty much useless. While it’s true that data isn’t sorted in any hierarchical order, it is still straightforward to access.

Data lakes use object-based storage, which makes each data piece “unique” by marking it with metadata and an identifier. This metadata contains essential information about each data piece, including its use and function, making it distinct from any other piece of information in the same data pool.

For this reason, a classic hierarchical structure becomes redundant, as it becomes impossible to confuse two data pieces — each has its metadata and identifier to serve as its “fingerprint.” As a result, data lakes can store both structured and unstructured data in a single place and are an excellent choice for various tech stacks.

Advantages of a data lake

This oversimple one-size-fits-all approach to managing data has many benefits:

  • Open format — data lakes are open formats, which not only future-proofs your operations, as you will never rely on a centralized, third-party provider, but also allow you to take advantage of the latest technologies.
  • Superb accessibility — object storage means each piece of data will remain just as accessible, no matter the size. Data is stored in a raw, unprocessed format, which means any app, device, or user can access it.
  • Easy storage access to any type of data — data lakes allow you to store data of all kinds in a single location, whether structured or unstructured. This means you can store multimedia files such as videos and images together with binary files, batch data without worrying about (in)compatibility.
  • Reduced costs — compared to other types of data storage, such as data warehousing, data lakes are much cheaper to maintain.

Disadvantages of data lakes

While there are some clear benefits data lakes offer, there are a couple of drawbacks we need to take into consideration:

  • Performance issues with increased lake size — even though object-based infrastructure makes it easy for apps/sites/devices/users to pull the necessary data, as the lake grows, keeping everything in a single place can become problematic if you don’t know what you are doing.
  • Security risks — because data lake is an open format and keeps everything in one location, it can be challenging to stay on top of everything as the amount of data grows. Maintaining, updating, and deleting data can become problematic, especially if you do everything on your own.
  • Less mileage — data lakes are a newer way to store big data and don’t have the same mileage as some other storage solutions, such as data warehouses. That means they are less time-tested, and there are less information and experts available.

Data lake vs. data warehouse

Data warehouses are pretty much the exact opposites of data lakes. Like in real warehouses, data is sorted hierarchically with relational logic. Think of data warehouses as the classic folder/subfolder/file storage structure.

Each piece of information is stored “cleanly” as it is processed during storage and tied to a particular, predefined use, ready to be queried. Because of this, warehoused data has excellent performance. Each piece has a predetermined location and uses, making it easy for the end-user to pull the information, no matter the warehouse size.

This type of structuring is useful for operational analysis and transactional processing, which is why data warehouses are used in most old-school businesses.

While it might seem beneficial to have data stored in a hierarchical manner, data warehouses have some limitations:

  • Lack of scalability — although data warehouses contain error-free data ready to be queried, this rigid schema makes data warehouses much harder to scale.
  • Resource-intensive — data warehouses process data upon entry, which requires a lot of computing power. Because of this, as the warehouse grows, its resource requirements will also increase costs. This is unlike data lakes, which process data only upon request, allowing you to save resources if the data pieces are unused.
  • Less flexibility — unlike data lakes which can store information from social media, IoT devices, websites, and mobile apps in a single location, data warehouses need to be configured for specific predetermined uses, making them far less flexible.
  • Increased pricing — the closed, proprietary format means the cost of data warehousing will grow with the amount of stored data. That will mean increased costs as time passes and your business grows.

Three best data lake providers

AWS Data Lake

Amazon Web Services is a widely popular Amazon cloud solution with outstanding data lake capabilities. In fact, Amazon S3 is cloud object storage, making it a perfect option for data lakes, as we previously discussed.

Data Lake on AWS architecture simplifies things to the end-user as it automatically configures the cloud servers for data lake use. Users get a convenient, intuitive console that lets them easily search through and request, analyze and transform data sets, as well as configure tags.

There are several advanced AWS Suite services available to use with data lakes, including AWS Lambda (for expanding functions), Amazon OpenSearch (for enabling advanced search options), Amazon Cognito (for user authentication), Amazon Athena (for analysis), and AWS Glue (for transformation).

Because everything works on S3, which is well-known for its scalability, you will never run out of resources or features, making AWS cloud lake a future-proof decision.

All in all, choosing AWS is never a mistake, as it’s a well-established name in the cloud industry, used by some of the biggest companies out there. It has excellent performance, an intuitive console, and a plethora of powerful Amazon apps that will soothe the needs of even the most demanding enterprises.

Azure Data Lake

Azure is Microsoft’s cloud infrastructure with all the capabilities required for running a data lake. It features unlimited data lake size, allowing endless scalability and ensuring even the biggest enterprises can run on Azure platforms without any issues. Azure claims you can store petabyte-size files and trillions of objects while maintaining maximal performance and security.

Azure Data Lake is a fully managed solution, which means you will get support around the clock. They also offer guarantees, ensuring your data remains available at all times. Azure has data encryption, both on-server (HSM keys) and while in transit (SSL), which, combined with multifactor authentication and role-based access controls, ensures your data remains secure.

What’s also great about Azure Data Lake is that it is a part of the Microsoft Cortana Intelligence ecosystem. Suppose you are already working with apps such as Power BI, Azure Synapse Analytics, Visual Studio, Apache Spike, Hive, Storm, or use any kind of Azure SQL. In that case, this data lake will be a perfect choice and will integrate into your existing tech stack seamlessly.

Azure Data Lake has a flexible pricing structure as you can choose to pay for on-demand clusters or a pay-per-job model, which is a better option if you are only going to use the solution sparingly. Because of this, and because all data lakes are open formats, so there are no licenses or recurring fees, you can keep your overhead costs to a minimum.

All in all, Azure Data Lake is a flexible solution that will never let you down and is especially suitable for scaling as it can handle large volumes of data with massive file sizes.

Google Cloud Data Lake

Google Cloud allows you to build a cloud-native data lake, speeding up the access of your analytics and engineer teams without having to upgrade any hardware on-premise.

It works with AI Platform Notebooks but with other non-Google services, such as Apache Spark, BigQuery, GPUs, and other accelerators. In fact, you can migrate any Apache Spark and Hadoop data lakes to Google Cloud, no matter the size, enabling you to have a full-managed data lake cloud solution. Migration is super easy, as you can configure and start the process in as little as 90 seconds.

There are dozens of Google data lake partners with full integration. That means you can expect your data lake to easily integrate and be fully accessible with any app in your tech stack — this is Google, after all.

Lastly, the Google Cloud data lake has flexible pricing with automatic scaling. That means you will never pay for resources you don’t use, but simultaneously, limited resources will never bottleneck your performance and stand in the way of your company’s growth.

The bottom line is that Google Cloud is an excellent data lake provider, with a good number of integrations with non-Google tech stacks and scalable resources, built on the reliable Google server infrastructure, making it a solid pick.

Blockchain, AR, VR, Metaverse, headless CMS, it really can be overwhelming to stay on top of all the changes.

Bottom line

As you can see, data lakes and data warehouses are two distinct ways to get the most out of big data. Both have their benefits — in a nutshell, data warehouses offer a traditional, time-tested, but costly way to organize data, while data lakes are more flexible and cheaper but require a more profound understanding of new technologies.

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